





"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A			      =	logging.get_logger(__name__)

A			      =	'''▁'''

A			      =	{'''vocab_file''': '''sentencepiece.bpe.model'''}

A			      =	{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

A			      =	{
    '''facebook/nllb-200-distilled-600M''': 1_024,
}

# fmt: off
A			      =	['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	VOCAB_FILES_NAMES
					__lowerCAmelCase	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__lowerCAmelCase	=	PRETRAINED_VOCAB_FILES_MAP
					__lowerCAmelCase	=	['''input_ids''', '''attention_mask''']

					__lowerCAmelCase	=	[]
					__lowerCAmelCase	=	[]







					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase="<s>"						,      _UpperCAmelCase="</s>"						,      _UpperCAmelCase="</s>"						,      _UpperCAmelCase="<s>"						,      _UpperCAmelCase="<unk>"						,      _UpperCAmelCase="<pad>"						,      _UpperCAmelCase="<mask>"						,      _UpperCAmelCase=None						,      _UpperCAmelCase=None						,      _UpperCAmelCase=None						,      _UpperCAmelCase = None						,      _UpperCAmelCase=None						,      _UpperCAmelCase=False						,      **_UpperCAmelCase						,      ):
									# Mask token behave like a normal word, i.e. include the space before it
									__a			:    List[Any]      							=	AddedToken(_UpperCAmelCase						,      lstrip=_UpperCAmelCase						,      rstrip=_UpperCAmelCase  ) if isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) else mask_token

									__a			:    Optional[Any]      							=	{} if sp_model_kwargs is None else sp_model_kwargs
									__a			:    Any      							=	legacy_behaviour

									super().__init__(
									    bos_token=_UpperCAmelCase						,      eos_token=_UpperCAmelCase						,      unk_token=_UpperCAmelCase						,      sep_token=_UpperCAmelCase						,      cls_token=_UpperCAmelCase						,      pad_token=_UpperCAmelCase						,      mask_token=_UpperCAmelCase						,      tokenizer_file=_UpperCAmelCase						,      src_lang=_UpperCAmelCase						,      tgt_lang=_UpperCAmelCase						,      additional_special_tokens=_UpperCAmelCase						,      sp_model_kwargs=self.sp_model_kwargs						,      legacy_behaviour=_UpperCAmelCase						,      **_UpperCAmelCase						,      )

									__a			:    Any      							=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(str(_UpperCAmelCase  )  )
									__a			:    Tuple      							=	vocab_file
									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
									# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
									# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

									# Mimic fairseq token-to-id alignment for the first 4 token
									__a			:    int      							=	{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									__a			:    Dict      							=	1

									__a			:    Optional[Any]      							=	len(self.sp_model  )
									__a			:    Dict      							=	{
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCAmelCase  )
									}
									__a			:    Dict      							=	{v: k for k, v in self.lang_code_to_id.items()}
									__a			:    str      							=	len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
									__a			:    List[Any]      							=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
									__a			:    Union[str, Any]      							=	list(self.lang_code_to_id.keys()  )

									if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													self._additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in self._additional_special_tokens]  )

									__a			:    List[str]      							=	src_lang if src_lang is not None else '''eng_Latn'''
									__a			:    List[Any]      							=	self.lang_code_to_id[self._src_lang]
									__a			:    List[str]      							=	tgt_lang
									self.set_src_lang_special_tokens(self._src_lang  )







					def __getstate__(    self  ):
									__a			:    Dict      							=	self.__dict__.copy()
									__a			:    Optional[Any]      							=	None
									__a			:    int      							=	self.sp_model.serialized_model_proto()
									return state







					def __setstate__(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	d

									# for backward compatibility
									if not hasattr(self						,      '''sp_model_kwargs'''  ):
													__a			:    Dict      							=	{}

									__a			:    List[str]      							=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )







					@property
					def 			_lowerCamelCase		(    self  ):
									return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token







					@property
					def 			_lowerCamelCase		(    self  ):
									return self._src_lang







					@src_lang.setter
					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    Optional[int]      							=	new_src_lang
									self.set_src_lang_special_tokens(self._src_lang  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = None						,      _UpperCAmelCase = False  ):

									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_UpperCAmelCase						,      token_ids_a=_UpperCAmelCase						,      already_has_special_tokens=_UpperCAmelCase  )

									__a			:    List[Any]      							=	[1] * len(self.prefix_tokens  )
									__a			:    List[str]      							=	[1] * len(self.suffix_tokens  )
									if token_ids_a is None:
													return prefix_ones + ([0] * len(_UpperCAmelCase  )) + suffix_ones
									return prefix_ones + ([0] * len(_UpperCAmelCase  )) + ([0] * len(_UpperCAmelCase  )) + suffix_ones







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = None  ):
									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = None  ):
									__a			:    Dict      							=	[self.sep_token_id]
									__a			:    str      							=	[self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep  ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      **_UpperCAmelCase  ):
									if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
									__a			:    List[str]      							=	src_lang
									__a			:    Dict      							=	self(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase						,      return_tensors=_UpperCAmelCase						,      **_UpperCAmelCase  )
									__a			:    Any      							=	self.convert_tokens_to_ids(_UpperCAmelCase  )
									__a			:    int      							=	tgt_lang_id
									return inputs







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	{self.convert_ids_to_tokens(_UpperCAmelCase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									return self.sp_model.encode(_UpperCAmelCase						,      out_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									__a			:    Tuple      							=	self.sp_model.PieceToId(_UpperCAmelCase  )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    int      							=	''''''.join(_UpperCAmelCase  ).replace(_UpperCAmelCase						,      ''' '''  ).strip()
									return out_string







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = None  ):
									if not os.path.isdir(_UpperCAmelCase  ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
													return
									__a			:    List[str]      							=	os.path.join(
									    _UpperCAmelCase						,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(_UpperCAmelCase  ) and os.path.isfile(self.vocab_file  ):
													copyfile(self.vocab_file						,      _UpperCAmelCase  )
									elif not os.path.isfile(self.vocab_file  ):
													with open(_UpperCAmelCase						,      '''wb'''  ) as fi:
																	__a			:    int      							=	self.sp_model.serialized_model_proto()
																	fi.write(_UpperCAmelCase  )

									return (out_vocab_file,)







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = "eng_Latn"						,      _UpperCAmelCase = None						,      _UpperCAmelCase = "fra_Latn"						,      **_UpperCAmelCase						,      ):
									__a			:    Optional[int]      							=	src_lang
									__a			:    Tuple      							=	tgt_lang
									return super().prepare_seqaseq_batch(_UpperCAmelCase						,      _UpperCAmelCase						,      **_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									return self.set_src_lang_special_tokens(self.src_lang  )







					def 			_lowerCamelCase		(    self  ):
									return self.set_tgt_lang_special_tokens(self.tgt_lang  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	self.lang_code_to_id[src_lang]
									if self.legacy_behaviour:
													__a			:    Any      							=	[]
													__a			:    Dict      							=	[self.eos_token_id, self.cur_lang_code]
									else:
													__a			:    Dict      							=	[self.cur_lang_code]
													__a			:    Optional[Any]      							=	[self.eos_token_id]







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    Dict      							=	self.lang_code_to_id[lang]
									if self.legacy_behaviour:
													__a			:    List[Any]      							=	[]
													__a			:    str      							=	[self.eos_token_id, self.cur_lang_code]
									else:
													__a			:    List[str]      							=	[self.cur_lang_code]
													__a			:    Union[str, Any]      							=	[self.eos_token_id]

"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	Dict      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


